SpecTec’s voice. Maritime Big Data: expectations vs reality

Big Data, Smart Ships, and Talking Ships: undoubtedly these are all hot topics in most of the recent and upcoming conferences in relation to the Maritime Industry.

But who in the Maritime Industry is actually ready to take on the challenge of mastering Big Data and possibly win “the prize”?

Had I to choose my favourite definition in place of the “Big Data” concept, I would rather use the more comprehensive (and thus well-fitting) definition of “Data Science”. Why I hear you say? If we are going with the definition of Big Data, this is expressed as:

“Data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges”.

However, the real challenge doesn’t lie in how/where this large amount of data is stored and managed; it’s actually in how it is cleansed, structured and analysed to make it useful for strategic decision-making.

The “Big Data” problem prediction, phrased by researchers Michael Cox and David Ellsworth of Nasa in 1997, was born in the field of space-engineering, when massive information generated by supercomputers could not be processed and visualized by the technology of that time.

Since then, computing capacity and information technology has tremendously evolved, and yet the difficulty/challenge of the manipulation and management of such big data is still there, especially affecting the Maritime Industry, where the technological tools, human skills and cultural approach are not and might not yet be mature for this kind of revolution.

The value of Maritime Big Data analytics can only be realized when an organizational and cultural change happens, necessarily accompanied by the appropriate analytical tools, skills and practices to put them to good use.

Compared to the Aerospace industry, the Shipping sector is not seen as a high-tech industry, but there are attempts at “filling the gaps”, one being the start of a data revolution. Ships have already started “talking” through sensors, exchanging high volumes of data through the satcom system, system connection and so on…

Data exchanged mainly comes from voyage data (Voyage Data Record), ship structure, components and machinery, but the official ‘all-purpose’ of data analysis is mainly addressed to fleet optimization and cost efficiency.

Compared to the volume of data collected, exchanged and analysed in the Aerospace industry, the amount of data exchanged as well as the technologies used to capture-store-manage-analyze by the Maritime industry is still far from reaching the level and purpose foreseen by the Big Data definition. Still, Data Science and in particular Data Analytics principles and algorithms can and should be applied.

Four main challenges pop up when we speak about Marine Big Data, which are:

  • sound competitive conditions (the privacy issue),
  • human resources (specialists who handle and analyse data),
  • technology (data integration tools and collection bases)
  • Data quality (security and reliability).

Some Classification Societies such as LR, DNV-GL and NKK, and International organizations like IMO, are investigating the challenges and advantages of a structured collection, storage and analysis of big data in the Maritime Industry, facing those 4 challenges that seem to hinder the “dream”.

They believe that Big Data analytics will enable the industry to drastically enhance the ship’s safety and environmental protection. Big Data will feature in all the strategic aspects of the business life-cycle of a ship: from design to manufacturing, operation and decommissioning.  However, it appears that the only way to achieve this ambitious result would be to enforce a centralized control of the entire process.

The issue is not generating data - data are created by fleet’s operations, its interactions with third parties and all the activities going on in its shore offices -  but there are lots of insights in such information that remain underrate.

It is about turning valuable data into actionable insights and knowledge, using it for the purpose of operating efficiency, revenue generation and improving services.